Chemistry-Inspired Pattern Formation with Robotic Swarms

TL;DR

This paper introduces a chemistry-inspired, decentralized stochastic method for robotic swarms to form emergent patterns, modeling interactions with Gibbs Random Fields and local rules, demonstrated through simulations and physical robot experiments.

Contribution

It presents a novel approach combining chemical interaction principles with Gibbs Random Fields to enable decentralized pattern formation in heterogeneous robot swarms.

Findings

Successful pattern formation in simulations

Feasibility demonstrated with physical robots

Versatility in creating various shapes

Abstract

Self-organized emergent patterns can be widely seen in particle interactions producing complex structures such as chemical elements and molecules. Inspired by these interactions, this work presents a novel stochastic approach that allows a swarm of heterogeneous robots to create emergent patterns in a completely decentralized fashion and relying only on local information. Our approach consists of modeling the swarm configuration as a dynamic Gibbs Random Field (GRF) and setting constraints on the neighborhood system inspired by chemistry rules that dictate binding polarity between particles. Using the GRF model, we determine velocities for each robot, resulting in behaviors that lead to the creation of patterns or shapes. Simulated experiments show the versatility of the approach in producing a variety of patterns, and experiments with a group of physical robots show the feasibility in potential applications.